The extremity of this range is better put into context by comparison with the average rating of human assessors shown the same reference translation, each labeled SAME in Figure 3, where the range of average ratings attributed to human assessors shown the same reference can be as large as 0.97 (G5).

(2013), since DA is a monolingual evaluation of MT that operates via comparison of MT output with a reference translation, it is therefore still possible, while avoiding other sources of bias, that DA incurs reference bias where the level of superficial similarity of translations with reference translations results in an unfair gain, or indeed an unfair disadvantage for systems that yield translations that legitimately deviate from the surface form of reference translations.


The different system outputs for each source sentence were grouped together to reduce the burden on the annotators.

a second example of lexico-syntactic divergence is that of "crossing movement" verbs.


7 WMT BLEU scores correlate poorly with challenge-set performance.

this strategy also necessitates that we work on fewer sentences.


For example, the French translation of the pronoun it in I hear an owl but I can't see it is translated as le or la, depending on whether owl is translated as masc.

the second set of features are manually defined, based on language-specific question-response patterns and recognisable lexical clues.


We can see that the node trajectories closely track each other (particularly at the beginning of the sentence) for Portuguese and Catalan, and in general the languages where objects precede verbs have higher average values, which would be expressed by our mean cell state features.

these models train a recurrent neural network LM (RNNLM; Mikolov et al.


We noticed that Uzbek and Uyghur are very similar, sharing a sizable amount of vocabulary, and several morphological rules.

accordingly, the drawback to this approach is the difficulty of finding any parallel data, let alone millions of sentences.


As expected, the results obtained with this straightforward approach show positive trends, but at the same time reveal its limitations for all the languages involved.

a standard clustering algorithm is then run on top of the VerbNetspecialised representations using vector dimensions as features to learn verb clusters (Sect.


Annotators were given a short written manual with instructions.

in sum, our experiments show that using annotations projected onto English text from parallel Czech text as cheap additional training data is a step forward to creating better models for the task of classifying telicity of verb occurrences.


From the perspective of Monte Carlo simulation, the advantage of this modification can be explained by viewing reweighting as a multiplicative control variate (Swaminathan and Joachims, 2015b).

our experiments present evidence to the contrary.


Given a similarity threshold τ , iff In this way, entities in C p are interconnected if they are similar in semantics.

the seed relation instance collection is defined as the total runtime complexity is O(k|L p | 2 ).


Tables 3 and 4 show the results of incorporating Brown cluster features into our trained models.

we take all (lowercased, stemmed) product tokens and keep those occurring at least 4 times in the training dataset (recall that these datasets are ≈ 700 posts).


This indicates that Brown clusters might be a useful feature sometimes, but do not solve the domain adaptation problem in this context.

we are interested in cases where a relatively small number of labeled posts (less than 100) might provide substantial benefit to the adaptation; a researcher could plausibly do this annotation in a few hours.


See supplemental material for more details.

a mention is starting at the next word (S) 2.


• Theoretically, we show that unlike a recently proposed state-of-the-art model that we compare against, our model does not exhibit the spurious structures issue in its learning procedure.

(2007) proposed a cascading approach using multiple linear-chain CRF models, each handling a subset of all the possible mention types, where the models which come later in the pipeline have access to the predictions of the models earlier in the pipeline.


From such empirical results we can see that our proposed model using mention separators consistently yields significantly better results (p < 0.01) than the mention hypergraph model across these two datasets, under two setups (whether to optimize F 1 -score or not).

due to space limitation, we provide a proof sketch here.


For more details on the dataset preprocessing, please refer to the supplemental material.

this is a way to balance the precision and recall of the model.


Important directions include additional annotation types (e.g., temporal/spatial), use of background knowledge, better handling of collocations, the use of nested representations, and multilingual OIE.

the factuality is independent from the factuality of the extracted triple; it indicates whether the supplier expresses a negation or a possibility.


To find such evidence, MinIE searches the relation for (1) modal verbs such as may or can, (2) possibility-indicating words, and (3) certain infinitive verb phrases.

minIE does not and cannot correctly minimize all its extractions in all cases.


An analysis of confusion patterns show that the most frequent type confusions are between PROCESS and MATERIAL.

reducing the amount of in-domain data hurts performance.


Augenstein and Søgaard (2017) use a multi-task learning strategy to improve the performance of supervised keyphrase classification, but they only report dev set performance on SemEval Task 10, we also include their result here and refer it as MULTITASK.

we observe that ULM+GRAPHFEAT* can greatly reduce the confusion, with 3.5% relative improvement of PRO-CESS and 3.6% relative improvement of PROCESS over ULM+GRAPHINTERP on token level.


On the other hand, due to insufficient labeled training data, deep models usually fail to behave normally in such domain, and state-of-the-art methods in these domains are usually dominated by rule based deductive methods or shallow model with hand-crafted features.

cLc achieved state-of-the-art performance in general NER, and therefore we take the cLc as our student network, Fig.


Therefore, in many special domains, only trained models or APIs are available, while their training data are private and inaccessible.

in NiTE, existing NER models behave like inefficient teachers to teach a deep neural network (we called student network) to identify named entities by giving it concrete examples.


Our framework can also incorporate crowd knowledge easily, in which the teachers can be human crowds, and then the NITE can employs active learning (Olsson, 2009) or lifelong machine learning (Chen and Liu, 2016) to progressively polishing the student model.

due to insufficient labeled training data, deep models usually fail to behave normally in such domain, and state-of-the-art methods in these domains are usually dominated by rule based deductive methods or shallow model with hand-crafted features.


For a sentence and its associated constituency parse, our system first sets three features for each node: a POS, a word, and a head.

algorithm 1 shows the recursive procedure called for the root node of a parse.


In response, we present Iterated Dilated CNNs (ID-CNNs), which instead apply the same small stack of dilated convolutions multiple times, each iterate taking as input the result of the last application.

when performing prediction using independent classification, the ID-CNN consistently outperforms a bidirectional LSTM (Bi-LSTM), and performs on par with inference in a CRF with logits from a Bi-LSTM (Bi-LSTM-CRF).


Evaluation Setup We evaluate our approach on the following four datasets: CoNLL-YAGO (Hoffart et al., 2011), ACE 2004(NIST, 2004Ratinov et al., 2011), ACE 2005(NIST, 2005Bentivogli et al., 2010), and Wikipedia (Ratinov et al., 2011).

prior work has shown that, for a given mention, aggressively pruning the set of possible entities to a small subset hurts performance only negligibly, while making the linker extremely efficient.


(2015) show using CrossWikis gives improved prior scores and candidate recall.

our model performs better than the neural-LSTM model of Shimaoka et al.


Equation 8 and 9 from within the contextual similarity model (Sec.

our novel approach learns representations of named entities ( A, B), context words and the relation vector R, then explicitly measures two aspects of the similarity: 1) relational similarity between entities and relation (Sec.


ity", "daily exercises", or "men with diabetes".

the dataset is human annotated, and each instance provides one sentence which includes two named entities and a relation type between the two entities.


Her speech contains a lot of words that are incorrect in standard German, however, the goal is it to preserve this "errors" in the digital version.

since DH projects do not evolve around the development of tools but the analysis performed with the help this tools in order to answer a specific question, the tools are expected to be delivered in an early phase of collaborative projects.


An example judgement document is shown in Figure 1, where we highlight the indicator clauses that we used to divide a document into three pieces and extract fact description, articles, and charges from each piece, respectively.

we thus adopt a two-step approach, specifically, we first build a fast and easy-to-scale classifier to filter out a large fraction of irrelevant articles, and retain the top k articles.


the semantic regions learned from the Embedded vMF Allocation model provide a set of candidates frequently mentioned by documents in the corpus.


These questions are constructed by taking one document from the corpus as d i and another document not from the corpus as d j .

our model is proposed independently for the purpose of identifying semantic focuses, which serves the task of outlier detection.


Document A is a significant outlier, and VMF-Q and VMF-E also agree with our intuition.

the cumulative probability distribution of a Poisson-Binomial distribution can be efficiently calculated by dynamic programming (Chen and Liu, 1997).


We confine ω to be a small integer less than 1% of the size of |D s |.

if we simply take the average, these two documents become indistinguishable as the average is substantially dominated by the "tail" where most words or phrases in either documents are clearly not orthodox.


In order to model the heterogeneous MOOC data, we develop a cascade representation model.

we observe that the video clips of a MOOC would have many well-formed subtitles composed by instructors.


Each data practice consists of a selection of a category (i.e., a theme associated with the practice, such as "First Party Collection/Use"), a set of values for attributes specific to the category, and text spans from the policy associated with the value selections .

we present a two-stage architecture of classification models to identify opt-out choices in privacy policy text, labelling common varieties of choices with a mean F1 score of 0.735.


In case the structure of the sentence is not preserved, the above heuristic fails, and we tag the sentence pair as Transformational Paraphrase.

often there is a need to review the edits made to the original document, which can be a long and arduous task.


Our analysis of the document corpus revealed that most text edits to sentences do not significantly change the structure of the sentence.

after the turker finishes reading the article, he is presented with a list of sentences that were changed between the initial and the final version, along with the changed sentences.


These aim to capture the importance associated with deletion, insertion or modification of specific POS tags and Named Entities.

in our work, we focus on automatically inferring the impact/change introduced by edits, and predict the perceived importance of such edits by authors.


Transition-based framework has been widely exploited in a number of other NLP tasks, including syntactic parsing (Zhang and Nivre, 2011;Zhu et al., 2013), information extraction (Li and Ji, 2014) and joint syntactic models (Zhang et al., , 2014.

reparandums are more difficult to detect, because they can be in arbitrary form.


More specifically, we would like to select the candidate sentence that has the smallest LD from the binary sequence created by the detection model: 1 True and false positives: Given the binary sequences described above, we also use as a feature the ratio of true positives (TP) to false positives (FP) by treating the error detection model as the "gold standard".

for each SMT system, we generate the list of all the 10 best candidate hypotheses.


table 2 shows CHARCNN+DOM is stronger on lower frequency replacements than WORD models.


In practice, we normalized the LSA word vectors to make them have unit length for SVM and PCA, but did not applied it to word2vec.

annotated as more formal 0.4 how are you?


We propose to use lexical formality models to control the formality level of machine translation output.

the final formality score for r is enforced to be zero: The neutral word is typically selected from function words.


For instance, phrasal MT systems were able achieve single-threaded decoding speeds of 100-500 words/sec on decade-old CPUs (Quirk and Moore, 2007), while Jean et al.

the original computation U c i requires total |T | × b multiplications per sentence, but the re-factored version U s j only requires total |S| multiplications.


We propose three strategies to integrate the history representation D into NMT: Initialization We use D to initialize either NMT encoder, NMT decoder or both.

while our approach is built on top of hierarchical recurrent encoder-decoder (HRED) (Sordoni et al., 2015), there are several key differences which reflect how we have generalized from the original model.


Similarly, we solved 75% of the Inconsistency errors including lexical, tense and definiteness (definite or indefinite articles) cases.

similarly, we solved 75% of the Inconsistency errors including lexical, tense and definiteness (definite or indefinite articles) cases.


We encourage the outputs of the common BLSTM to be language-agnostic by using language-adversarial training (Chen et al., 2016) inspired by domainadversarial training (Ganin et al., 2016;Bousmalis et al., 2016).

this objective encourages the BLStM layers and the embedding layers to learn linguistically general-purpose representations, which are also useful for specific downstream tasks (Rei, 2017).


They showed better average accuracies than target only cases, but they showed mixed results.

the loss function of the POS tagging can be formulate as: where S is the number of sentences in the current minibatch, N is the number of words in the current sentence, p i,j is the label of the j-th tag of the i-th sentence in the minibatch, andp i,j is the predicted tag.


The Multi30K dataset has 29k, 1k and 1k images in the train, validation and test splits respectively, and contains two types of multilingual annotations: (i) a corpus of one English description per image and its translation into German; and (ii) a corpus of five independently collected English and German descriptions per image.

the authors would like to thank the anonymous reviewers for their helpful comments.


Since the ordering of the systems is random, system scores obtained by averaging scores of all sentences translated by the system would be unbiased, assuming a sufficiently large sample of sentences.

as shown in Table 5 we can see that the sentences in the "low" partition and the "high" partition have a difference of 0.18, which is highly significant; 3 moreover, this difference is likely to be sufficiently large to alter the rankings of systems in an evaluation.


For the purpose of the current approach, we limit our search to a single hop away, but this index can be easily expanded to support a wider search.

the textual reference to the entity (entitytext in q) needs to be linked to an actual entity node in our KB.


Other work has considered the empirical effects of some of the more arbitrary-seeming algorithmic choices (Levy et al., 2015).

to determine whether observed properties are due to SGNS specifically or to embeddings in general, we compare SGNS-trained vectors to vectors trained by the GloVe algorithm (Pennington et al., 2014).


These inner products are negative, indicating that the context vectors point in the opposite direction from the word vectors.

in practice, these samples appear to find an equilibrium that effectively balances out the number of random samples after a short burn-in phase.


It is generally assumed that the geometry of word embeddings is determined by semantic relatedness.

the large majority of interactions are between frequent terms or between frequent and infrequent terms.


The mini-batch size is fixed to 32 and we perform a grid search for the other hyperparameters, tuning against the task-specific evaluation metric on held-out data, with early stopping.

(2016), we train models for the same set of 13 languages from the Universal Dependency treebanks v1.1 (Nivre et al., 2016) corpus, using the standard predefined splits.


See their following works (Wang et al., 2017a,b).

compared with previous studies, our MIN has following features: • co-occurrence pattern between aspects and opinions is captured via memory interactions, where the neural memory operations are designed to summarize task-level information and perform interactions.


As shown in Table 3, removing bi-directionality decreases the extraction performances (-2.0% and -1.0%).

s-LsTM learns the sentimental representation h s T of the sentence and then feeds it in aspect prediction as a soft constraint: On the whole, our proposed MIN framework has three LsTMs and each of them is differentiable.


It is also important to understand how such an approach will work on other types of models such as neural networks based NLP models.

our approach learns embeddings for the target domain by capturing useful cross-domain information and therefore can lead to improved modeling of embeddings that are shown more helpful for this specific down-stream task.


G is the interaction graph that is iteratively constructed during the focused reading procedure, with V being the set of vertices (biochemical entities), and E the set of edges (promotion/inhibition interactions).

(2016) focus on slot filling using a pre-existing template.


Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways.

for example, RL has been used to guide IR and filter irrelevant web content (Seo and Zhang, 2000;Zhang and Seo, 2001).


The existing dataset by Deri and Knight (2015) contains 401 portmanteau examples from Wikipedia.

experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.


As features, we used the extraction confidence, length of arguments and relations as well as part-of-speech tags, among others.

common task definitions and comparable evaluations are missing.


Studies have shown that self expression and social support are beneficial in improving the individual's state of the mind (Turner et al., 1983;Choudhury and Kiciman, 2017) and thus such communities and interventions are important in suicide prevention.

we also report a baseline result which is based on a SVM classifier with bigram features.


Social media data are often sensitive, and even more so when the data are related to mental health.

to the above works, our model does not use any general or domain specific feature engineering; it learns appropriate representations of documents by considering only their textual content.


Furthermore, several verbs such as driving, shooting, and coaching are heavily biased toward a male agent.

lagrangian relaxation and dual decomposition techniques have been widely used in NlP tasks (e.g., (Sontag et al., 2011;Rush and Collins, 2012;Chang and Collins, 2011;Peng et al., 2015)) for dealing with instance-level constraints.


MS-COCO is gender biased In Figure 2(b) along the x-axis, similarly to imSitu, we analyze bias of objects in MS-COCO with respect to males.

if o is positively correlated with ), we say bias has been amplified.


The three modified training methods designed as defenses have a positive effect on privacy.

we perform topic classification on two genres of documents: news articles and blog posts.


As a result, improving the privacy of the neural representations comes at a cost in accuracy.

overall, we found that the multidetasking approach lead to the more stable improvements and should be preferred in most cases, since it is also the less computationnally expensive defense.


Independently of its explicitness, some of this private information correlates with the output labels, and therefore will be learned by the network.

improving the privacy of the neural representations comes at a cost in accuracy.


This is the worst possible scenario for the attacker, as it is training on the exact representations the adversary attempted to remove the protected attribute from.

for example, with 5 adversaries, the main-task accuracy remained at random for 5 epochs, and only begun rising at the 6th epoch.


(Edwards and Storkey, 2015;Feutry et al., 2018).

weight we experiment with different weighting of the adversarial component during training by tuning the λ parameter, trying the values 0.5, 1.0 (default), 1.5, 2, 3, 5 (with values above 5 the main task training became extremely unstable, not raising above 50%).


The attacker's rates are lower than in Table 1, indicating the adversarial training is effective in removing some of the protected attribute.

table 3 summarizes the attacker's recovery rate on the adversarialy-trained encoders for the different settings.


Word2vec (Mikolov et al., 2013), Skipthought vectors (Kiros et al., 2015), Contextualized Word Representations (Melamud et al., 2016;Peters et al., 2018) etc.).

the leakage can be caused due to one of them (or due to their combination).


We observe that DeClarE (Full) outperforms all the other approaches -thereby, re-affirming its power in harnessing external evidence.

their model heavily relies on handcrafted language features.


This was not an effect of corpus size, since these corpora contained 800,000 and 1.1 million tweets, respectively, compared to the 1 million in our unlabeled corpus.

we experimented with the efficacy of our domainspecific resources, the impact of different context parameters, and the contribution of context to predicting Aggression and Loss.


In the second sentence, two analogy frames are plotted together, one reflecting the absolute values of the stock prices mentioned (blue) and the other reflecting the changes in prices mentioned (red).

mapping from an utterance to a TAP frame requires three types of relational reasoning.


The model not only misclassifies the QSRL role of 'daily contracts traded', but also mistakenly identifies an EQUIVALENCE between 'this year' and 'the year earlier'.

our model of textual analogy is particularly influenced by Structure Mapping Theory (Falkenhainer et al., 1989;Gentner and Markman, 1997), an influential cognitive model of analogy as a structurepreserving map between concepts.


We develop a new dataset of TAP frames from quantitative newswire, and compare a variety models for TAP.

the VALUE 9,118 is left without a compared content role, and is dropped.


11 Video data has other language differences; notably, character names in LSMDC were replaced by 'someone' 12 The most predictive individual words on SWAG are infrequent in number: 'dotted' with P(+|dotted) = 77% with 10.3 counts, and P(−|similar) = 81% with 16.3 counts.

this work was supported by the National Science Foundation Graduate Research Fellowship (DGE-1256082), the NSF grant (IIS-1524371, 1703166), the DARPA CwC program through ARO (W911NF-15-1-0543), the IARPA DIVA program through D17PC00343, and gifts by Google and Facebook.


For a single pair (s i , x), we utilize the function f int () in Equation 4 to build the fine-grained representations for both s i and x, obtaining For (S e , x), we compose all the {i i } and all the {x i } along with i, via a weighted max-pooling: This weighted max-pooling ensures that the sentences with higher probabilities of being evidence have a higher chance to present their features.

then the (S e , x) pair gets a coarse-grained concatenated representation: [e, x].


To achieve this, we propose to reconstruct the vector of associated word from the corresponding multimodal semantic representation.

the parameters of word association module are this additional criterion drives the learning towards a semantic representation capable of reconstructing its associated representation.


As evidenced by a range of evaluations (Andrews et al., 2009;Bruni et al., 2014;Silberer et al., 2016), multimodal models can learn better semantic word representations (a.k.a.

existing models ignore either the associative relations among modalities, associative relations among relative words, or the different contributions of each modality.


When generating chair, the topic attention is focused on a wrong object bed, while the visual attention attends correctly to the chair, and especially the output attention attends to the armrest.

the visual attention is expected to find the most relevant image regions in generating the caption.


Distance error takes place when there is a lot of objects and the model cannot grasp the foreground and the background relationship.

it not only complicates the extracting procedure but also contributes little to the generation.


for videos in ActivityNet Captions, we sample every second.


Two hyperedges {T k i → I k i,i } and {T k i → X} from T k i indicate that either there exists at least one mention starting with the i-th word (the former hyperedge), or there does not exist any such mention (the latter hyperedge).

for decoding, we perform the standard MAP inference on top of the complete hypergraph to find the most probable hyperpath.


As the authors have mentioned in their papers, for a given sub-structure in their model, there exist multiple ways of interpreting the combination of mentions.

the final representation for i-th word is the concatenation of them: Next, we use the a bidirectional LStM to capture contextspecific information for each word, resulting in the word-level features: Such representations are then used as inputs to a second LStM to generate span-level features: Inspired by (Kong et al., 2016), we compute all possible span embeddings efficiently with time complexity O(cn) using dynamic programming, with n being the number of words in the input x and c being the maximal length of a mention.


However, based on such local predictions one could also interpret "A B C" as a mention -this is where the ambiguity arises.

note that this approach cannot model the overlapping mentions of the same type.


Empirically, we find that the length restriction (c=6) improves the precision of semi-CRF and SH at the expense of the recall, providing some evidence to support the hypothesis.

only neural models without using external features are included.


4 We adopt a log-linear approach to model the conditional probability of each hyperpath as follows: where f (x, y) is the score function for any pair of input sentence x and output mention combination y, which corresponds to a unique hyperpath G y .

in terms of F 1 , the unrestricted semi-CRF performs worse while unrestricted SH performs better compared to their restricted counterparts.


There has been a great deal of work on using variational autoencoders in semi-supervised settings Maale et al., 2016;Zhou and Neubig, 2017;Yang et al., 2017b).

since the z variable is used to both predict the tag and reconstruct the word, it must capture both the tag and word reconstruction spaces, and may be limited by capacity in doing so.


As reconstruction loss, we use cross-entropy over the words in the vocabulary.

since they are both used for reconstruction, the two spaces show signs of alignment; that is, the "tag" latent variable y does not show as clean a separation into tag clusters as the y variable in the VSL-GG-Hier model in Figure 3e.


2 We choose English and Chinese as example languages because they are top-ranked according to total number of speakers, the full list can be found in We use multi-lingual Wikipedia as KB including a set of entities E y = {e y i } and their articles.

our embeddings are helpful to break down language gaps in many tasks, such as cross-lingual entity linking, in which the major challenge lies in measuring the similarity between entities and corresponding mentioned words in different languages.


Moens (2015, 2016) collect comparable documents on same themes from multi-lingual Wikipedia, shuffle and merge them to build "pseudo bilingual documents" as training corpora.

concrete mono-EN by adding edges from al entity e e i to e z j if < e e i , e z j >∈ R e−z e We utilize distant supervision to parable sentences from Wikipedia shown in Figure 1, from the page ar lingual linked entities e e Kobe and e z K those sentences including another linked entities e e Joe and e z Joe as co tences S e−z = {< s e k , s z k >}.


On one hand, we utilize their shared semantics to align similar words and entities with similar embedding vectors, no matter they are in the same language or not.


e , w , w The intuition is that, words and entities in various languages share some common semantic meanings 1 , but there are also ways in which they differ.


We consider both words and entities, which makes the parallel data issue more challenging.

researchers leverage both types of resources to improve various natural language processing (NLP) related tasks, such as relation extraction (Weston et al., 2013;Lin et al., 2017), and entity linking (Tsai and Roth, 2016;Yamada et al., 2016;Ji et al., 2016).


e , w , w The intuition is that, words and entities in various languages share some common semantic meanings 1 , but there are also ways in which they differ.

we naturally extend mono-lingual function to cross-lingual settings: where C ′ (e y i ) denotes cross-lingual contextsneighbor entities in different languages that linked to e y i .


Our approach achieved significantly higher performance than state-of-the-art multilingual embedding learning methods through both intrinsic and extrinsic evaluations.

many languages also share linguistic properties, e.g., apposition, conjunction, and plural suffix (English (-s / -es), Turkish (-lar / -ler), Somali (-o)).


After we project the monolingual embeddings into the common semantic space, we further reconstruct M l 1 and M l 2 from H l 1 and H l 2 separately: are cross-lingual reconstructions.

monolingual embedding quality evaluation Table 6 shows the name tagging performance for each language using the original monolingual embeddings and multilingual embeddings.


1 More than 3,000 languages have electronic record, e.g., at least a portion of the Christian Bible had been translated into 2,508 different languages.

corrNet can project multiple monolingual word embeddings into a common semantic space using bilingual word alignment.


For instance, there are a few pairs where BWE-Pivot outperforms BWE-Direct, such as de-it, itde and pt-de, even though it goes through a third language (English) in BWE-Pivot.

furthermore, recent work proposes approaches to obtain unsupervised BWEs without relying on any bilingual resources (Zhang et al., 2017;Lample et al., 2018b).


Luminoso (Speer and Lowry-Duda, 2017) and NASARI (Camacho-Collados et al., 2016) are the two top-performing systems for SemEval-2017 that reported results on all language pairs.

the idea of Multilingual Pseudo-Supervised Refinement (MPSR) is to induce a dictionary of highly confident word pairs for every language pair, used as pseudo supervision to improve the embeddings learned by MAT.


This paper proposes a modularized sense induction and representation learning model that jointly learns bilingual sense embeddings that align well in the vector space, where the crosslingual signal in the English-Chinese parallel corpus is exploited to capture the collocation and distributed characteristics in the language pair.

every bilingual sense induction module will experience the degradation during the training process presented in Algorithm 1.


The optimization is driven by mini-batches of ATTRACT pairs B A (batch size k A ), and of REPEL pairs B R (size k R ).

we test if the specialization knowledge from a resoure-rich language (i.e., English) can be transferred to resource-lean target languages (see §2.3).


Empirically it was found that such meta-embeddings can often outperform the individual word embeddings from which they were obtained.

the higher scores indicate that the two monolingual embedding spaces become more tightly integrated because of our additional transformation.


An iterative two-step procedure is also implemented in order to avoid the need of starting with a large seed dictionary (e.g., in the original paper it was tested with a very small bilingual dictionary of just 25 pairs).

simply dropping the orthogonality constraints leads to overfitting, and is thus not effective in practice.


What can we learn about language by observing the editing process that we cannot readily learn by observing only the final edited text?


To do this, we focus our attention specifically on the English, Spanish, and German subcorpora, as these are languages for which we could find a sufficient number of native speakers to perform the necessary annotation for our analysis.

we release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning.


Moreover, fine-grained typological features such as exponence, flexivity, fusion, and inflectional synthesis are borne out to be responsible for the proliferation of low-frequency phenomena which are organically difficult to model by statistical architectures, or for the meaning ambiguity of character n-grams.

only through finer-grained typological features it becomes possible to justify several outliers, as shown in the next subsection.


For multilingual LM evaluation, Botha and Blunsom (2014) extract datasets for Czech, French, Spanish, German, and Russian from the 2013 Workshop on Statistical Machine Translation (WMT) data (Bojar et al., 2013).

flexivity indicates the possibility that the value of a grammatical category be mapped into different morphological forms (1:many).


Unseen test words are mapped to one <UNK> vector, sampled from the the space of trained word vectors relying on a normal distribution and the same fixed random seed for all models.

the results for all three benchmarked language models on all 50 languages are summarised in table 3.


More specifically, our approach uses two 2-layer GCNs, and each GCN processes one KG to generate embeddings of its entities.

increasingly more research work studies the problem of cross-lingual KG alignment, aiming to match entities in different languages in a multilingual KG automatically.


As a common practice, we choose the well established Skip-gram model to obtain monolingual word embeddings.

and sememes can help us comprehend human languages better.


The "common space" variant performs the worst by a large margin, confirming our hypothesis that discrepancy between the two embedding spaces harms the model's ability to generalize.

in this paper, we closely follow the architecture proposed by Lample et al.


Cross-lingual NER attempts to address this challenge by transferring knowledge from a high-resource source language with abundant entity labels to a low-resource target language with few or no labels.

these approaches utilize such features by training a model on the source language and directly applying it to the target language.


These decoders generate tokens from left to right, at each step giving a distribution over possible next tokens, conditioned on both the input and all the tokens generated so far.

as our model is agnostic to both the model architecture and the target metric, we see the exploration of more diverse and ambitious model-target metric pairs as a clear avenue for future work.


Neural machine translation (NMT) based on the encoder-decoder architecture becomes the new state-of-the-art due to distributed representation and end-to-end learning (Cho et al., 2014;Bahdanau et al., 2015;Junczys-Dowmunt et al., 2016;Gehring et al., 2017;Vaswani et al., 2017).

these results show that our method could rectify more words (51 vs. 70) with less deterioration (17 vs. 11) than Arthur(test).


The source word x j is an exception (a candidate troublesome word) if (x j , y i ) is an entry in the word alignment A 2 .

our work uses memory to store contextual knowledge in the training data.


Third, we design a dynamic strategy to balance the memory and NMT.

the contextual memory contains five elements: each of them is described as follows: • s m is a troublesome source word.


they wanted to go back in time, they wanted to go back into the before, before there had cars or twitter visitors.

certain words in the output translation, like verbs, are crucial to the understanding of the target language sentence but only provide marginal value in n-gram matching evaluations like the BLEU score.


Apparently, we need to trade off the efficiency for the accuracy.

the external bilingual data are usually not in the same domain as the utterance, and hence they are not aligned with the slot-value pair and system acts (i.e., (c e s , c e v , a e t ) or (c f s , c f v , a f t )).


The encoder-decoder abstraction makes it conceptually feasible to build a system that maps any source sentence in any language to a vector representation, and then decodes this representation into any target language.

next, we describe related work, in terms of the parameter generator for nMT: • Pairwise: In the simple and commonly used pairwise nMT setting (Wu et al., 2016;Crego et al., 2016), the parameter generator would generate separate parameters, θ (enc) and θ (dec) , for each pair of source-target languages.


Low-Resource: Similar to the supervised experiments except that we limit the size of the parallel corpora used in training.

this is based merely on the fact that this is the largest model size we could fit on one GPU.


Inspired by topicbased SMT, some researchers resorted to incorporating topical contexts into NMT.

a famous open-source NMT system used widely in the NMT community trained on mix-domain training set.


Since domain-specific and domain-shared contexts have different effects on NMT, and thus should be distinguished and separately captured by NMT model.

• +Domain Control (+DC) (Kobus et al., 2016).


(2016) enhanced the word representation by adding its topic embedding.

furthermore, we applied the hypertools (Heusser et al., 2018) to visualize the sentence representations E r (x) and E s (x), and the domain-specific and domain-shared annotation sequences Here we represent each annotation sequence with its average vector in the figure.


Furthermore, we conducted several visualization experiments to empirically analyze the individual effectiveness of the added model components.

to ensure encoding accuracy of domain-shared contexts, we follow  to adopt an alternative two-phase strategy in training, where we alternatively optimize J (D; θ) with θ s1 adc and {θ-θ s1 adc } respectively fixed at a time.


In this way, domain-specific target words will be assigned greater weights than others in the objective function of our model.

we first jointly model NMT with monolingual attention-based domain classification tasks.


In the previous section, we have formulated the induction of a bilingual lexicon as the search for an edge set E, which we treat as a latent variable that we marginalize out in equation 2.

the latent-variable model yields gains over several previous approaches across language pairs.


Each language consists of a set of words each parameterized by a word vector.

we selected the solution with the smallest unsupervised reconstitution criterion.


Another support for the plausibility of unsupervised word translation came with the realization that when words are represented as vectors that encode co-occurrences, the mapping between two languages is well captured by an affine transformation (Mikolov et al., 2013b).

a major recent trend in unsupervised learning is the use of Generative adversarial Networks (GaNs) presented by Goodfellow et al.


The sequential property of RNNs leads to its wide application in language processing.

y n according to the word-level chain rule resulting in decoding each word depending on all previous decoding results, thus hindering the parallelizability.


Figure 1 shows that sampling and beam+noise outperform the MAP methods (pure beam search and greedy) by 0.8-1.1 BLEU.

this shows that back-translation with sampling can result in high-quality translation models based on benchmark data only.


However, BT-news still improves the baseline by up to 1.2 BLEU.

the previous experiment showed that synthetic source sentences generated via sampling and beam with noise perform significantly better than those obtained by pure MAP methods.


Figure 2 shows that synthetic data based on source Diese gegenstzlichen Auffassungen von Fairness liegen nicht nur der politischen Debatte zugrunde.

bT-news still improves the baseline by up to 1.2 bLEU.


It is tempting to disallow the model from generating hypotheses which are not in the dictionary N .

a new generation model G t+1 is trained for the next iteration using the union of the seed list S and the mined name pairs B (line 12).


This work was supported by the National Natural Science Foundation of China (NSFC) via grant 61632011, 61772156 and 61502120.

the evaluation metric was case-insensitive BLEU (Papineni et al., 2002)  to effectively train the NMt model, we trained each model with sentences of length up to 50 words.


an attention model is utilized to calculate the context vector for each machine system, while the other attention model obtains the context vector over the all context vectors of machine systems.


Showing remarkable superiority over conventional statistical machine translation (SMT), NMT has been recognized as the new de facto method and is widely used in commercial MT systems .

most existing work on document-level NmT has focused on integrating document-level context into the RNNsearch model (Bahdanau et al., Context • • •ziji ye yinwei queshao jingzheng duishou er dui saiche youxie yanjuan shi• • • Source wo rengran feichang rezhong yu zhexiang yundong.


We further demonstrate that these challenges cannot be overcome by a simple domain adaptation approach alone.

the specific challenges for neural machine translation have not been studied until recently (Belinkov and Bisk, 2018;Sperber et al.


Similarly, in French, our model over generates "de votre" (where "votre" is the formal 2nd person plural for "your") and "n'ai pas" which showcases the "ne [.

we pass our data through spell checkers to count spelling and grammar errors.


The annotator might reasonably contribute the question "who wrote gulliver's travels?"

to better understand the remaining 6.5% gap, we do an empirical error analysis on a sample of 50 negative examples.


Finally, we report an empirical analysis showing that the upperbound is loose.

adding all of these pairs to the data is problematic.


The selected relevant text regions (∈ ψ r ) are first passed through a separate BiGRU, then we apply a selfattention mechanism similar to the Context Zoom layer followed by a linear layer with ReLU activations.

our model achieved the best performance on the challenging NarrativeQA dataset.


The main idea of most word embedding models follows the distributional hypothesis (Harris, 1954), i.e., the embedding of each word may be inferred using its context.

x (2) ij = CO(i, j) represents context overlap, a word independent learnable scale A is adopted to relieve the potential inconformity between first order and second order co-occurrences.


Our Bag-of-Substring (BoS) word vector generation model views a word as a bag of its substrings, or character n-grams.

our BoS model shows steady and significant gain compared to MIMICK embeddings for both tasks in all languages.


table 7 shows an ablation study on input representations (with or without ELMo vectors).


only on 4 out of 21 languages are type constraints better.


All data sources employed in our experiment are very high-coverage.

there is a large 33-point accuracy gap between the scores of Li et al.


We propose a method to strike a balance between model simplicity and the capacity to easily integrate heterogeneous learning signals.

for true low-resource languages, we cannot safely assume the availability of all disparate information sources.


The SMI is an fdivergence (Ali and Silvey, 1966).

our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods.


The j-th taskspecific language adversarial network estimates language labels from the j-th task-specific hidden representations.

the cross-entropy loss functions of each network are defined as: where L o , L x , and L y are the cross entropy loss terms for the classification networks, the task adversarial networks, and the language adversarial networks.ô n,i are the reference probabilities, and o n,k , x n,j , and y n,i are the estimated probabilities of the k-th label in the j-th task classification network, the j -th task in the ith language-specific task adversarial network, and  the i -th language in the j-th task-specific language adversarial network for W n , respectively.


Luong et al (2015) proposes local attention that averages a window of input.

this is concordant with the recent experiments of (Niculae and Blondel, 2017).


(2017), which includes two layers: a BiLSTM layer which extracts a sequence feature for each input word, and an attention layer which represents the input sequence using weighted words.

in Table 4, JMECause outperforms the best pipeline model (LSTM) by 0.8% in F-scores.


Figure 1 shows evidence that a sentiment tool would not suffice on accurately predicting tweets with pessimistic and optimistic connotations (left and right side of the figure, respectively).

as mentioned, a tweet was considered optimist if its manually annotated score was above 1 and pessimist if the score was below −1.


The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates.

in the proposed model, the size of hidden units is 128 and all word embeddings are initialized by GloVe (Pennington et al., 2014) of 300d.


In order to explore limitedresource scenarios, we experiment with a range of training set sizes.

since the least-square approximation is not perfect, we find that in practice fitting a classifier on the approximation D train • T works better than usingD train , acting as a regularizer.


We then use the three classifiers to predict the author attributes of the remaining authors in the test data set.

the retrofitting process changes the configuration of the embedding space (intoD train ), so a separating hyperplane learned onD train will not be applicable to a test set D test in the original embedding space.


Since the intent of a dialogue utterance may depend on its topic or some frequent key words, utterances in the same intent may share similar context features.

in this paper, we instead propose our framework auto-dialabel to automatically cluster the dialogue intents and slots.


Since each F i has different dimensions, which may unequally affect the clustering results, we use an autoencoder to encode all F i into same dimensions as E i , then we concatenate all the E i as E, which will be used in the clustering procedure, as shown in Figure 2.


Fluency rep-  resents whether each sentence is in correct grammar.

it is expected that the AEM+Attention model achieves the best G-score.


Optimizing for this metric enables controlling the trade-off between precision and recall with a threshold, depending on the end-application needs.

this simple method can be used as a starting point for future works on this dataset.


The high correlations we obtain between the metrics indicate that this may be the case.

(2002) showed that BLEU correlates with human judgments in the ranking of five English-to-Chinese MT systems and that it can distinguish human and machine translations.


c i is the summarization for the post regarding to the hidden state s i−1 : where v 1 is the attention parameter.

we know that there are still many directions to enrich the proposed framework.


The generation ends if a special token *end* is predicted.

actor-critic (Sutton and Barto, 1998) methods are often adopted, which involves training an additional value network to predict the expected reward.


To address this problem, (Ranzato et al., 2015) proposed a Reinforcement Learning (RL) based training framework.

in this work, to tackle these two issues, we propose a simple yet effective solution by introducing coherent language constraints on local action selections in RL.


Actor-critic (Sutton and Barto, 1998) methods are often adopted, which involves training an additional value network to predict the expected reward.

2 We use an Ima-geNet pre-trained 101-layered ResNet 3 (He et al., 2016) to extract visual features.


In addition, captions generated by our models are more human readable and graceful.

learning with RL is a notoriously difficult task due to the high-variance of gradient estimation.


We first train for 25 epochs with crossentropy (XE) loss, using Adam with learning rate 5 • 10 −4 .

the main paragraph captioning dataset is the Visual Genome corpus, introduced by Krause et al.


Table 2 compares the effect of training with 1 We believe the improvement may be largest on CIDEr because, in the calculation of CIDEr, n-gram counts are clipped to the number of times each n-gram appears in the reference sentence.

the paragraph captioning models proposed by Krause et al.


The readability score is based on grammaticality, structure, and coherence.

beyond enhancing the evaluation prowess of ROUGE, due to its lexico-semantic analysis of summaries, we believe that ROUGE-G has the potential to expand the applicability of ROUGE to abstractive summarization.


The WordNet graph has edges of various types, with the main types being hypernymy and meronymy to connect nodes containing senses.

our graph-based approach (RoUGE-G) computes semantic similarity scores between n-grams, along with their match counts, to perform both semantic and lexical comparisons of peer and model summaries.


We aim to help ROUGE with identifying the semantic similarities of linguistic items, and consequently tackling the main problem of its bias towards lexical similarities.

conventional word sense disambiguations are not applicable due to the lack of contextual information.


Quantifying the quality of summaries is an important and necessary task in the field of automatic text summarization.

law and dress are spuriously similar because of the node tailor.


In Table 3, we can see that our approach results 1 We were able to obtain comparable AMR summarization subgraph prediction to their reported results using their published software but not to match their bag-of-word generation results.

we also compare them against the baseline (unguided) model from §3.2.


In this paper we use the same principle and reward n-grams that are found in the source document during the AMRto-Text generation process.

to address this shortcoming, several works have explored the use of Abstract Meaning Representation (Banarescu et al., 2013, AMR).


EXTRACT also outperforms previously published extractive models (i.e., SummaRuNNer, EXTRACT-CNN, and REFRESH).

(2018) equip the same architecture with a training algorithm based on reinforcement learning.


To summarize, in this paper we provide: • A quantitative analysis on how abstractive current neural document summarizers are by calculating various types of content overlap with the input documents.

automated summarization generally produces extractive summaries by selecting complete sentences from the source document (Nenkova et al., 2011) to ensure that the output is grammatical.


We also demonstrate that such a query well-formedness classifier can be used to improve the quality of a sequence-to-sequence question generation model (Du et al., 2017) by showing an improvement of 0.2 BLEU score in its performance ( §3).

we use the evaluation package released by Chen et al.


Users can view and edit a list of anchor words (or rather, sets of words which form each anchor), and they can view the top ten most probable words for each topic.


Obtained from https://github.com/ alexandres/lexvec.

without introducing topic coherence as a training objective, topic modeling likely produces sub-optimal results.


Each character x i in the source sentence x is mapped to the corresponding d ce -dimensional row vector c i of a learnable d voc × d ce embedding matrix, initialized with a random uniform distribution with mean 0 and variance 1.

we experiment with both character embeddings and pre-trained word embeddings, using several embedding models, and we achieve a state-of-theart F 1 score on an Arabic spelling correction task.


1 The source code and corpora generated by this research are available at https://github.com/ abhijith-athreya/ASDUS.

we name this the coverage of the output.


A potential weakness of MLE is the mismatch between b p(X, Y ) and the true data distribution p(X, Y ).

this optimization problem has a tractable analytic solution, which describes a generic framework of which both word dropout and RAML are instances.


we insert words only before-not after-a position, since an extra word after the ending word (usually a punctuation) is not probable.


We use this heuristic to augment the score estimation (SCORE): (3) ν(f ) is the percentage relative frequency of the ciphertext symbol f , while ν(e) is the percentage relative frequency of the plaintext token e in the plaintext language model.

the most widely used technique is the use of beam search with n-gram LMs proposed by Nuhn et al.


In our experiments our LRLs are all selected to have an helper that is highly similar in both aspects, but choosing an appropriate helper when this is not the case is an interesting problem for future work.

we contrast a number of strategies, leading to a novel, simple, yet effective method of "similar-language regularization", where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data.


The service can be expected to store models for a large number of users and dynamically load and adapt models for many active users concurrently.

the overall best performing compact adaptation technique, group lasso regularization, is further evaluated on six other language pairs trained using production data sets collected from Lilt's user base: English↔French, English↔Russian and English↔Chinese.


Instead of treating an RNN and a CNN as isolated components, our work directly marries RNNs with the convolution operation, which illustrates a new direction in mixing RNNs with CNNs.

the key phrase hit rate is then defined as the ratio of filter-detected m-grams that fall into the corresponding key phrase sets.


The recurrent layer learns a set of hidden states corresponding to different time steps, and the max pooling layer extracts the most salient information from the hidden states.

the assumptions become more problematic if we increase the window size m. We propose to address the limitations by employing RNNs to realize convolution filters, which we term recurrent neural filters (RNFs).


We apply dropout to embedding layers, pooling layers, RNN input layers, and RNN recurrent layers.

we define the key phrases for a sentence to be the set of phrases that are labeled with the same sentiment as the original sentence.


For unaligned words, we alter all COPY rules in the extracted n-grams to copy tokens by their aligned counterpart, such as replace "params" with "lst", and delete the n-gram subtree, as it is not likely to be relevant in the predicted tree.

some attempts using neural networks have used sequence-to-sequence models (Ling et al., 2016) or tree-based architectures (Dong and Lapata, 2016;Alvarez-Melis and Jaakkola, 2017).


The corpus was constructed by crawling the websites of international organizations such as the Canadian government, the European Union, and the United Nations.

the SPR properties are diverse, and we expect to find gains by choosing training settings on a property-specific basis.


We initially determine whether or not the final sentence of the subject is that by a king, as some issues are dealt with by others, such as the king's mother.

when we add speaker information, the performance increases even with random initialization of speaker embedding.


Instead of training the transferred model by ourselves, in this paper, we adopt the ELMo word representations , which are derived from a bidirectional language model (BiLM) trained on one billion word benchmark corpus (Chelba et al., 2014).

in fact, some traditional features, such as the POS tags or parse trees, naturally provide strong signals for identifying EDUs.


Conditional Random Fields (CRF) (Lafferty et al., 2001) is an effective method to sequence labeling problem and has been widely used in many NLP tasks (Sutton and McCallum, 2012).

we propose a restricted self-attention mechanism, which only collects information from a fixed neighborhood.


bandwidth) or physically ("blind") disabled people.

if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication.


We use a fully connected layer with 1024 ReLU hidden units as our answer generator.

alent to the last visual "attention map" although there isn't explicit visual attention in our imagecomprehension pipeline).


Feature-modulating methods like FiLM (De Vries et al., 2017;Perez et al., 2018) control image-comprehension process using modulation-parameters generated from the question, allowing models to be trained end-to-end.

questions in CLEVR cover several typical elements of reasoning: counting, comparing, querying the memory, etc.


The scoring function is g t = h t × A × r, where A ∈ R E×E is a square matrix and r ∈ R E×1 is a relation vector.

assumed that there are E predefined relations (including Na) to extract.


We have developed a program PageTagger 2 to assist the annotation.

also, some researchers like to group their publication by year or by topic.


To filter such strings, we need to rely on indicators that may reside in a different line such as a heading "Dissertations supervised".

on publication string level, annotators agree on 83.76% publications, and the Cohen's kappa is 0.2084.


, x n and action history sequence is a 0 , a 1 , .

let z ik denote the k-th action for the i-th sentence, the loss function with 2 norm is: where λ is the 2 coefficient.


For example, disambiguating the identity of the song Down in Figure 1 is easy if we can recognize the nearby string Jay Sean in the context, but the model sometimes fails to do this.

because the CNNs process every example, they may not see enough examples of lexical overlap to pick up on it, and instead try to augment what the word embedding model is already doing with subword information, which ends up being unstable for this task.


To disambiguate between closely related concepts, entity linking systems need to effectively distill cues from a mention's textual context.

the character-level CNN model only places 60% of its attention weight on it, distributing its attention values more evenly across the rest of the words.


This work was supported in part by NSF, AFRL, DARPA, Siemens, Google, Amazon, ONR YIP and ONR Grants N00014-16-1-2713.

we evaluate if explanations help against a No IF baseline by providing an explanation only in the test phase, and no IF (see Fig 4; No IF + Explanation).


Here we present SALIE (Salient Information Extraction), the first fact salience system able to output a ranking of salient open facts from a text document.

text summarization techniques to score the atomic units can be exploited for fact salience.


It is trained to minimize: where θ is the set of all parameters associated with the model, and y is the correct answer.

all published experiments of the latter use 3 hops where each hop requires 2 separate Bi-GRUs, one  to model the document and one for the query.


Table 3 gives the details of the ablation study of the three SEQ2SEQ models.

several observations can be made from the results.


This is especially problematic with little data, as it often happens in question answering (QA) tasks, e.g., answer sentence selection (Wang et al., 2007;Yang et al., 2015).

for each question we collect the output of our question analysis CNNs.


This model includes relational information in terms of question focus, entities and question categories too, but uses additional syntactic information (i.e., syntactic trees).

the input and output resolutions are the same.


The question itself is encoded in a vector q as a weighted average of the hidden states of a BiLSTM over the word embeddings of its individual tokens.

we map the answers to unambiguous wikipedia pages using combination of rule based matching and fuzzy string matching, then filter out the questions whose answers are not mapped to any wikipedia page (12.5% of the questions).


Therefore, Equation (4) becomes: Each row in the RBF kernel matrixK is now interpreted as a feature vector, going from step S2 to step S3 in Figure 2.

more precisely, we propose two transductive learning approaches combined into a unified framework.


Using this labeling scheme, in the restaurant data, 1034 out of 1117 test points have the same sentencelevel and aspect-level labels.

among 100 parameterized filters with size 3, 25 of them are generated by aspect CNN with filter size 1, 2, 3, 4 respectively.


The basic statistics of our two datasets are summarized in Table 2.

table 1 shows three example sentences along with their emotion labels.


the Self-Annotated Reddit Corpus (SARC) was created by Khodak et al.


This baseline does not take author information into account: for each comment, only the words of the comment are considered as inputs.

the positive examples are essentially those which the authors considered ambiguous enough to explicitly tag as sarcastic, meaning that the prediction problem is actually to identify which comments are not only sarcastic but both sarcastic and not obviously so.


We have also produced a set of sentences that can be used to test future analyzers.

finally, we highlight the importance of assessing performance of sentiment analyzers using practical sentences that involve not only negation as in previous studies (Socher et al., 2013), but also polarity items such as the 150 sentences used in our experiment.


The U.S. data come from the yearly birth records maintained by the U.S Social Security Administration from 1880 to 2013; the U.K. data come from the UK Office of National Statistics, the Northern Ireland Statistics and Research Administration, and the Scotland General Register Office.

overall, all models predicted gender of people's names substantially above chance (50%).


Our experiments show that our re-rank strategy increases the document recall compared to the single-step approach (Table 2).

our ranker outperforms the TF-IDF method by 38.8% in evidence retrieval F1, with 65 times faster inference speed, achieving a promising performance in a large-scale fact extraction and verification dataset.


To also harness the global information encoded in all posts in a subset, we build a Hybrid model which multiplies the scores from the Disjunctive model and the TextRank.

to utilize popularity as an indicator of informativeness, we need to exclude the effect of context.


(Rotter, 1966) to identify an external locus of control when authors express that they feel controlled by other people or the environment.

internal control is often associated with causing a given event or doing something that is clearly a choice.


To what extent could the sommelier profession, or wine stewardship, be displaced by machine learning algorithms?

we explored two solutions: (1) we separated our data into mono-varietals (Model 1), and blends (Model 2), and trained HSLDAd separately; (2) we created a smaller yet more balanced training set regarding mono-varietals and blends (Model 3).


(2014), which contains 1066 sentences from the breast cancer discussion board in the Cancer Survivors' Network (CSN) of the American Cancer Society, denoted as B-DS.

we ended up with 1041 sentences in L-DS.


This allows us to trivially find the most likely target sequence by taking arg max yt p(y t |X) for each t, effectively bypassing the computational overhead and suboptimality of decoding from an autoregressive sequence model.

the original input X is padded or shortned to fit the length of the reference target sequence before being fed to Decoder 1.


The proposed approach could instead be viewed as learning a conditional denoising autoencoder which is known to capture the gradient of the logdensity.

first, the generation quality improves across all the tasks as we run more refinement steps i dec even beyond that used in training (i train = 4), which supports our interpretation as a conditional denoising autoencoder in Sec.


The utterances are corrupted by background noise, and a large portion of them are accented.

the hypothesis is generated by a CtC-based (Graves et al., 2006) ASR system trained on WSJ training set.


Additionally, since TagLM (Peters et al., 2017) with one-layer NNs can be viewed as a special case of ELMo, we also include its results.

models marked with † employed LSTms with projection, which is more efficient than the vanilla LSTms.


We refer to their output as To ensure the model to predict valid label sequences, we append a first-order conditional random field (CRF) layer to the model (Lample et al., 2016).

for a L-layers NN, we mark the input and output of the l th layer at the t th time stamp as x l,t and h l,t .


To name a few: event detection (Li et al., 2013;Nguyen and Grishman, 2015;Peng et al., 2016), coreference Lu and Ng, 2017), temporal analysis (Do et al., 2012;Chambers et al., 2014), sequencing , script induction (Chambers and Jurafsky, 2008;Balasubramanian et al., 2013;Rudinger et al., 2015;Pichotta and Mooney, 2016).

learning can be done by standard gradient methods.


Event Mention Annotation: Despite many annotation attempts on events (Pustejovsky et al., 2002;Brown et al., 2017), automatic labeling of them in general domain remains an open problem.

adding all voting features improves all metrics.


Notice that if we set margin m τ to 0, the second case becomes an L1 loss |x −ŷ|.

this is convenient, because L τ is fastest to compute during training, as it requires no score calculation for each tLink type.


Considering an undirected graph G = (V, E) as the syntactic parsing tree for sentence W , and E are sets of nodes and edges, respectively.

with this additional gating mechanism, the final syntactic GCN computation is formulated as As stacking k layers of GCNs can model information in k hops, and sometimes the length of shortcut path between two triggers is less than k, to avoid information over-propagating, we adapt highway units (Srivastava et al., 2015), which allow unimpeded information flowing across stacking GCN layers.


Typically, highway layers conduct nonlinear transformation as: where σ is the sigmoid function; ⊙ is the elementwise product operation; g is a nonlinear activation function; t is called transform gate and (1 − t) is called carry gate.

when taking each token as the current word, we get the representation D from all tokens calculated by GCNs.


Then a self-attention aggregation mechanism is applied to aggregate the associations between events.

the input of the k-th GCN layers should be h (k) instead of h (k) .


Since dependency graph has 55 different edge labels, incorporating all of them overparameterizes the model significantly.

we concatenate the entity type embedding of target entities to the final bag representation before using it for relation classification.


(3) Our HBTNGMA outperforms feature-based collective methods (Li's Structure, Yang's JointEE and Liu's PSL), it proves that our automatically learned features can efficiently capture semantic information from plain texts.

if we know died triggers a Die event in S1, which is easier to disambiguate, we tend to predict that fired triggers an Attack event.


We inspect the two clusters and the overlapping region in Figure 5, it turns out that the nouns in the separated clusters are words that can appear as subjects and, therefore, for which verb agreement is important to model.

we repeat this process 5 times and report the mean and standard deviation of performance.


In this work, we define a novel generative approach to leverage continuous word representations for unsupervised learning of syntactic structure.

we constrain our neural projector with two requirements: (1) dim(x) = dim(e) and (2) f −1 φ exists.


8 Dan Bikel's randomized parsing evaluation comparator (Bikel, 2004) was used to perform significance tests on precision and recall metrics on WSJ §23 and CTB §271-300.

to perform a fair comparison, we define the novel dynamic oracles on the original implementations of the top-down parser by  and in-order parser by Liu and Zhang (2017a), where parsers are trained with a traditional static oracle.


Apart from improving the systems' accuracies in both cases, we achieve the best result to date in greedy shift-reduce parsing on the WSJ.

• Given a subset S of R(c, γ G ), the previously shown constituent decomposability property implies that there exists at least one transition sequence starting from c that generates the tree S ∪(γ c \γ G )∪E, where E is a set of erroneous constituents containing one such constituent per element of (α c \ α G ).


This will leave the parser in a configuration whose value of j is r, and where stack elements with left span index ≤ l (apart from those referencing the new non-terminal and its leftmost child) have not changed.

a static oracle trains the parser only on gold transition sequences, while a dynamic one can guide the parser through any possible transition path, allowing the exploration of non-optimal sequences.


Our work is grounded in the notion that by trying to explain the POS bigram counts in a target corpus, we can discover a stochastic realization policy for the target language, which correctly "translates" the source trees into appropriate target trees.

8 This pruning heuristic retains 36.1% of the trees (averaging over the 20 development treebanks ( §5.1)) for training, and 66.6% for actual realization.


The solid lines in Figure 2 show our improvements on the dev data, with a simpler scatterplot given by in Figure 7 in the supplementary material.

we design a dynamic programming algorithm which, for any given distribution p in wang and Eisner's family, can compute the expected counts of all POS bigrams in the permuted source treebank.


We allow s = BOS or t = EOS as defined in §2.4.2.

when a is a verb, the model can assign a positive weight to "subject precedes verb" or "subject precedes object," thus preferring orderings with these features.


Intuitively speaking, question-relevant explanations for images should be more likely to help predict right answers than irrelevant ones.

and the explanations also has key information that can predict the answer "tennis court."


There is a growing research interest in the task of visual question answering.

the fully-connected layer features of CNNs are usually uninterpretable to humans.


Ideally, we would like to see an increase in dialog success rate and a decrease in dialog length, as is the case when comparing the learned and static policies.

the agent asks a fixed number of queries before guessing.


Each recipe includes an arbitrary number of steps containing both textual and visual elements.

white wine makes an excellent compliment to the meal, especially if it is the same wine used in .


The negative answers in TVQA are written by human annotators.

future work also includes integrating better temporal cues.


We compare our bestperforming model from training on the TEMPO-TL (strongly supervised MLLC and conTEF) to prior work (MCN and TALL) and to MLLC with global and before/after context.

we rely on the preposition project (Litkowski and Hargraves, 2005) † to scrape relevant temporal words.


In the field of neural-based methods, Raganato et al.

this paper is supported by NSFC project 61772040 and 61751201.


Define , thus the outputs of i-th sentence-level co-attention mechanism are computed as Like Equation 10, we can also calculate a sentence-level score for the i-th word sense by a dot product of the context vector c s i and the gloss vector g s i : The sentence-level context embedding vectorĉ s is also computed as the average of N gloss-aware context vectors c s i : 3.4 Output Layer The output layer aims to calculate the scores of N senses of the target word x t and finally outputs a sense probability distribution over the N senses.

finally, the probability distributionŷ over all the senses of the target word is computed aŝ where λ xt ∈ [0, 1] is the parameter for word x t .


We create the POM for all annotated verbs using the same procedure as Del Tredici and Bel (2016).

for the sake of a meaningful evaluation, we ensured that 25% of these metaphorically used tokens were novel metaphors.


Since many word pairs in the benchmarks are dissimilar we keep all SOCO correlations for the benchmark word pairs in this experiment.

the overall picture is that our approach indeed is able to capture meaningful similarity relations, and that it performs comparably to regular first-order word embedding methods.


Instead, it defines the prior over word senses via a Dirichlet process.

next, they calculate average word vector for each context in the training corpus, cluster these context representations and relabel each word in the corpus to a word sense represented by the nearest cluster.


2013/09/B/ST6/01549 "Interactive Visual Text Analytics (IVTA): Development of novel, user-driven text mining and visualization methods for large text corpora exploration".

we extend the objective of our model (Eq.


(B) The dead were buried in the same grave under the authority of Commander Kamal.

as we show below, we can generate data that exhibits challenging inference problems by exploiting the Wikipedia category structure.


As a short hand, let the binary variable m w denote whether or not the word w has a visual representation.

interestingly, if a non-visual word w 1 shares a similar context c with a visual word w 2 , then the factor z w 2 will affect e w 1 via v c .


On one hand, the linguistic information can guide the extraction of latent visual factors.

for the models CNN, VAE and their concatenation with SGNS embeddings, the latter setting is not applicable.


Tables 2 and 3 present the accuracies of the different TL and MTL models on the development and test splits in NomBank and PCEDT.

x P CEDT = x N omBank , but the label sets are different Y P CEDT = Y N omBank .


For example, Fares (2016) reports that the temporal and locative relations in NomBank (ARGM-TMP and ARGM-LOC, respectively) and their counterparts in PCEDT (TWHEN and LOC) exhibit a relatively consistent behavior across frameworks as they annotate many of the same compounds.

in MTL, we train one model simultaneously to learn both PCEDT and NomBank relations, and therefore all the MTL models have two objective functions and two output layers.


(2015) trained a character LSTM language model on source code and showed that individual neurons in the hidden state track the beginning and end of code blocks.

as  showed the biLM based representations outperformed CoVe in all considered tasks, we focus exclusively on biLMs.


Our baselines are regular distributional models: GloVe (Pennington et al., 2014) and word2vec (Mikolov et al., 2013).

we aim to reduce the manual labor involved in designing the rules and directly obtaining representations from raw definitions.


This supports our hypothesis that dictionaries contain similarity-specific information.

we have a different goal: that of creating or improving word representations.


These widely-used representations often significantly improve performance in downstream tasks, as they are able to leverage large amounts of unstructured data.

in doing so, we embrace the suggestion of Gladkova and Drozd (2016), which argue for "a shift from absolute ratings of word embeddings towards more exploratory evaluations that would aim not for generic scores, but for identification of strengths and weaknesses of embeddings", and conduct rigorous analysis of each representation.


There are several possible reasons why this dataset may be more difficult for an embedding-based solver.

as K increases, ELMo clusters outperform the baseline, 8 https://github.com/attardi/ wikiextractor 9 https://spacy.io reaching its maximum performance at K = 5, indicating that a finer level of sense granularity is beneficial for focusing on the intended word sense.


The domains can be created by knowledge bases such as WordNet (Mason, 2004) or based on automatic clustering .

in all settings, the precision scores are lower compared with the recall scores.


For example, tb and vb indicate the beginning of a tenor and a vehicle respectively.

we make following contributions: • We build a dataset consisting of 11.3k sentences containing a frequently used comparator "oe" for simile recognition in Chinese, which can support data-driven approaches.


We first study the effectiveness of our model for answer sentence selection tasks.

analysis of the learned sentence structures shows they can reflect some syntactic phenomena.


Acknowledgments This research is supported by a Google PhD Fellowship to the first author.

these models amount to simply changing the input vectors a and b, and still only perform a wordlevel alignment between the two sentences.


We proposed a new neural architecture, CAFE for NLI.

factorization machines model lowrank structure within the matching vector producing a scalar feature.


The factorization layer tries to decompose the alignment vector (constructed from the variations of a − b, a b and [a; b]), learning higher-order feature interactions between each compared alignment.

note that each of the six factorization operations has its own parameters but shares them amongst all words in the sentences.


In this paper, we propose the Convolutional Interaction Network (CIN), a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI.

the convolutional filter should be dynamically changeable.


Table 5 shows that simply using of the embeddings trained on the MIMIC-III notes significantly increases the accuracy for all the models.

the maximum gain is 2.4%, 0.9%, and 0.3% for the BOW, In-ferSent, and ESIM models correspondingly.


Moreover, retrofitting works only on directly related concepts in a knowledge graph (although it might affect, to some extent, indirectly related concepts by transitivity).

transfer learning has been shown to improve performance on variety of tasks such as: machine translation on lowresource languages (Zoph et al., 2016) and also some tasks from the bio-medical domain in particular (Sahu and Anand, 2017; Lee et al., 2018).


The BLEU score evaluates the question generation.

after obtaining hidden states s t , we adopt the copying mechanism that predicts a word from the target vocabulary or from the source sentence (detailed in Subsection 4.2).


Different from WikiSQL, this dataset requires question-answer pairs for training.

we first sample SQL queries on the tables from wikiTableQuestions, and then use our QG model to generate question-SQL pairs.


A challenge here is that we do not know the samples before performing the policy-gradient method.

finally, we discuss how to measure semantic correctness.


We encourage the model to generate regular expressions to maximize expected rewards instead of MLE.

we convert a regular expression to a minimal DFA and utilize the fact that equivalent regular expressions have the same Algorithm 1: Policy-gradient method to maximize semantic correctness Input: Estimate ∇ θ J(θ) using (3.2) ; Update θ using ∇ θ J(θ) by gradient descent ; and generated string examples, where s 0 represents the start state and s 2 is the only accept state.


Herzig was supported by a Google PhD fellowship.

for this we use the pre-trained embedding function φ(•) as the only cross-domain information.


We evaluated the structure mapper by measuring the exact match of the top candidate in the beam to the gold abstract logical form (49.1%).

(2017) proposed to exploit structural regularities in language across different domains.


To deal with these issues, we use a greedy search that keeps the consistency among spans and can return multiple spans for adjunct labels.

we evaluate F1 scores only for the span boundary match, shown by Table 4.


In the same manner, by calculating the scores for all the spans S and labels R, we can obtain the score matrix (at the top part of Figure 1).

eLMo assigns different vectors to each word depending on the input context.


Our model can be regarded as an extension of their model.


(2017 apply an attention based seq2seq model similar to (Luong et al., 2015) to text-to-SQL tasks.

by passing the SQL history, each module is able to predict a different output according to the history every time it is called during the recursive SQL generation process.


Figure 3: Illustration of the model architecture.

their performance drops on the other metrics.


The training on unlabeled data proceeds otherwise in the same way as described in Algorithm 1.

contrast this with the traditional learning settings for semantic parsers, where the user (or annotator) provides the correct logical form y i or the execution of the correct logical form y i (denotation) (Table 1).


DeepCx usually leads Causeway by at least 5 points, making the difference highly statistically significant (p 0.001).

(This is why for each new connective anchor a, λ 4 starts out with a as its first element.


Likewise, λ 3 and λ 4 contain the indices of compared and uncompared words, respectively, to the right.

i indicates the instance under construction; thus, x i denotes an argument or fragment arc of instance i.


Restricting generalization One of the strengths of the transition-based approach is its ability to recognize previously unseen forms of causal language that resemble known connectives semantically and/ or linguistically.

the F 1 metrics reflect exact span matches; it is counted as a mismatch if even a single word is off.


The authors show that by having successful interactions with users, not only the accuracy of the candidate queries can be improved but also new insights into limitations of current query generation systems can be gained.

for the running example in figure 1, applying value abstraction maps the question to "Which award has the category of the ENTITY?"


For each condition in the SQL query, its column name appears in the question around the neighborhood of its condition value with exactly the same surface form.

study how to answer user questions with table cells from millions of HTML tables.


Then we report the final results by incorporating it into our parser.

• Non-Lexical: concepts which are invoked by their child concepts rather than from tokens in the sentence directly.


It yields the lowest crossing arcs rate since lots of concepts are not aligned.

nonlexical concepts country and name can be aligned to the span 16-17, which is "United Kingdom".


If the subject is labeled as an Entity, its class will be encoded in the feature vector.

a naive literal usage metrics is to choose a predefined threshold for all idioms and label all the instances with score above the threshold as literal usages.


In this paper our first aim is to assess the validity of word similarity tasks as proper evaluation tests for polysemic word representations.

in fact, the effect reported in Neelakantan et al.


Its binary features correspond to adverb edges from j u to j v and from j v to j u in JJGRAPH.

in general, scalar adjectives describing the same attribute can be ordered along a full scale (e.g.


but it mis-predicted 33% of pairs as uncertain because of its limited overlap with the IQAP vocabulary.


While paraphrase-based intensity evidence produces pairwise predictions that are less precise than those produced by pattern-or lexicon-based evidence, the coverage is substantially higher.

as with pattern-based approaches for other tasks (such as hypernym discovery (Hearst, 1992)), they are precise but have relatively sparse coverage of comparable adjectives, even when using webscale corpora (de Melo and Bansal, 2013;Ruppenhofer et al., 2014).


, x n } ⊆ X and a positive definite kernel k : X × X → R, a matrix A = (a 1 , .

as well as deriving PMI from mutual information, we derive this new measure from the Hilbert-Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC).


We also thank Weihua Hu for useful discussions, Kenshi Yamaguchi for collecting data, and Paul Reisert for proofreading.

mI(X, Y ) is the degree of dependence between X and Y measured by the KL divergence between P XY and P X P Y .


To address the summarization alignment, former studies try to apply an attention mechanism to measure the saliency/novelty of each candidate word/sentence (Tan et al., 2017), with the aim of locating the most representative content to retain primary coverage.

to our best knowledge, we make the first attempt to develop a neural data-driven solution for the automatic related work summarization, and the practice of using the joint context as an attention cue is also less explored to date.


Our convolutional encoder associates each word with a topic vector capturing whether it is representative of the document's content, while our convolutional decoder conditions each word prediction on a document topic vector.

in this section we present our experimental setup for assessing the performance of our Topic-aware Convolutional Sequence to Sequence model which we call T-CONVS2S for short.


Our topic-enhanced model calibrates longrange dependencies with globally salient content.

our model builds on the work of Gehring et al.


We focus on developing a summarization model that produces an increased level of abstraction.

2016, and the pre-processing steps of See et al.


We reward the policy with the ROUGE metric, which measures word overlap with the ground-truth summary, as well as a novel abstraction reward that encourages the generation of words not in the source document.

it also loses its novel unigrams and bigrams advantage.


given a document containing n sentences s 1 , .


We experiment with three variants for our WANE model: (i) WANE: where the word embeddings of each text sequence are simply average to obtain the sentence representations, similar to (Joulin et al., 2016;Shen et al., 2018c).

despite showing improvement over structure-only models, these semantic-aware methods cannot capture word-level alignment information, which is important for inferring the relationship between node pairs, as previously discussed.


Further, the word-by-context embedding for sequence t a is obtained by taking the weighted average over all word embeddings Intuitively, α i may be understood as the relevance score between the ith word in t a and sequence t b .

case study The proposed word-by-word alignment mechanism can be used to highlight the most informative words (and the corresponding matching features) wrt the relationship between vertices.


Although these methods have demonstrated performance gains over structure-only network embeddings, the relationship between text sequences for a pair of vertices is accounted for solely by comparing their sentence embeddings.

deepWalk (Perozzi et al., 2014), which learns vertex representations from random-walk sequences, similarly, only takes into account structural information of the network.


Given an input X, the filter-generation module can be implemented, in principle, as any deep (differentiable) architecture.

tables 3 and 4 show experimental results of our models on WikiQA and SelQA datasets, along with other state-of-the-art methods.


As illustrated in Table 2, S-ACNN significantly outperforms S-CNN on both datasets, demonstrating the advantage of the filtergeneration module in our ACNN framework.

in this paper, we consider an approach of using a small meta network to learn contextaware convolutional filters for text processing.


A recent work (Gupta et al., 2017) tries to learn task-specific embeddings for CLIR.

in training, the positive sample x d t+ for x qs can be chosen as the most relevant texts according to annotation, and the negative sample x d t− is picked randomly from the data collection.


The relatively worse performance of OE-GCN can be attributed to the fact that it only focuses on the Event-Time information and leaves out most of the contextual information.

they propose two models, one of which learns the probabilistic constraints between year mentions and the actual creation time, whereas the other one is a discriminative model trained on hand-crafted features.


The output hidden representation h v ∈ R d of a node v after a single layer of graph convolution operation can be obtained by considering only the immediate neighbours of v, as formulated in (Kipf and Welling, 2016).

predicting the time of these documents based on their content is an important task, often referred to as Document Dating.


We explain the approach in detail in the next Section.

most of these fall in one of two categories: methods that learn a mapping from one space to the other, e.g., as a least-squares objective (e.g., (mikolov et al., 2013)) or via orthogonal transformations Zhang et al.


(2017b) also leverage optimal transport distances for the cross-lingual embedding task.

for our second set of experiments, we consider the-substantially harder 3 -dataset of (Dinu et al., 2014), which has been extensively compared against in previous work.


To encourage the R2A-generated attention to be consistent with the provided rationales in aspect transfer, we augment the overall training objective of R2A in Eq.

we minimize the cross-entropy loss L lm over all source data and unlabeled target data.


We thank Abi See, Christopher Clark, He He, Peng Qi, Reid Pryzant, Yuaho Zhang, and the anonymous reviewers for their thoughtful comments and suggestions.

on an unlabeled example, the model first produces soft targets p θ (y|x i ) by performing inference.


In addition to the auxiliary prediction modules listed in Section 3, we find it slightly improves results to add another one that sees the whole input rather than a subset (but unlike the primary prediction module, does have dropout applied to its representations).

the supervised models only learn from taskspecific labeled data during the main training phase.


Finally, we conclude the evaluation with a performance check in a standard annotation task.

annotating data on the scale required to train state of the art systems using traditional expert annotation would be unaffordable.


much difference between GloVe GigaWiki14 and GloVe Giga.

we apply our algorithm with small adaptations to select antecedents for bridging anaphors on these corpora.


(2012) released a corpus called ISNotes which contains unrestricted bridging annotations.

this is because time expressions are related to a lot of words in the corpus in which we learned embeddings bridging from.


Note that we do not add the suffix " PP" to a bridging anaphor representing time information, because such an anaphor is likely to have the same semantic type antecedent (see Example 2).

we use a Naive Bayes classifier with standard settings in wEKA (witten and Frank, 2005) and apply the best first strategy to select the antecedent for each anaphor.


Because entities in the candidate sentences can contribute to the answer (unlike in the WSC), we modified the query generation rules to extract more than just predicates.

in general, our coreference heuristics filter strongly: over all Winograd instances, they filter a total of 50,110 retrieved sentences down to only 3,097 (0.0617 acceptance rate).


TF vectors refers to textually enhanced vectors with λ = 1.

4 handles subtle variations of each distinct sense due to polysemy.


The purpose of the classification task ( §4.3) is to provide a direct comparison of the textually enhanced vectors against vectors produced by the original graph, but independently of the compositional part.

we consider this as a strong indication for the effectiveness of the textually enhanced vectors as representations of KB entities.


This finding is consistent with previous work on simpler tensorbased models, which showed that applying some form of word sense disambiguation when composing word vectors can provide consistent improvements on end tasks such as sentence similarity and paraphrase detection .

for each word w i in a training example, a context vector c i is computed as the average of the generic vectors of all other words in the sentence.


For this paper we implemented a BiLSTM architecture.

the training data is selected from the other 4 folds.


In this task, we predict the time interval or the time instance τ for a given test triple (h, r, t, ?).

we represent time as a hyperplane i.e., for T number of time steps in the KG, we will have T different hyperplanes represented by normal vec- we try to segregate the space into different time zones with the help of the hyperplanes.


Now, given a quadruple (h, r, t, [τ s , τ e ]), we consider it to be a positive triple for each time point between τ s and τ e .

we draw negative samples from the set, The above mentioned loss L is minimized subjected to the constraints.


Assuming that there is little domain shift of input feature spaces, they simplify the problem as homogeneous transfer learning (Weiss et al., 2016).

the word adaptation layer serves as a way to bridge the gap of heterogeneous input spaces, but it does so only at the word level and is context independent.


From Table 6 we can see that the text comparison is consistent with star ratings.

with the meta-paths above, we use the Path Count feature defined in  to feed into the entity linking model described in Section 3.


The prevalence of on-line social networks has also motivated researchers to study entity linking in such environments.

among them, conventional features are those that can also be use in traditional entity linking tasks; social features and location features are unique in our problem.


Compared with traditional entity linking problems that link mentions to a knowledge base, this new problem have less information about the target entities.

there are also some interesting findings if we consider the performance on different categories.


Location features do not help here because Cafe Bellagio has the same location as Bellagio Hotel.

on social media platforms, most of these resources or information are either unavailable or of poor quality.


In the past, many traditional NLP engines have used rule based dictionary lookup methods, with Unified Medical Language System (UMLS) (Lindberg et al., 1993) as a base dictionary, to detect clinical entities.

we used a semiautomatic way to find domain related information and were able to classify 236,850 documents from all the documents into 40 different domains.


The last column is average user accuracy and label amount.

these categories are people (PER), organizations (ORG), geopolitical entities (GPE), and nationalities as well as religious/political affiliations (AFF), resulting in 6,567, 6,199, 1,617, and 422 entities per category, respectively.


These seed entities, labeled with ground truth rather than user labels, initialize the bootstrapping model, so that participants start off with identical embeddings.

we realize this using large-margin metric learning (Cui et al., 2016;Sohn, 2016), minimizing: where (a, b, c) represents a triplet of entities, such that a and b belong to the same category, while c belongs to a different category, and the function s is the cosine similarity between entity embeddings.


A disadvantage with the Skip Gram model is that it might fail to be discriminative, since it does not utilize category labels.

to ensure a representative sampling, kernel density estimation is performed within each cluster's entities, and the entity with highest density, along with its nearest neighbors in the embedding space, are selected.


SwellShark has no annotated data, but for entity span extraction, it requires pre-trained POS taggers and extra human efforts of designing POS tag-based regular expressions and/or hand-tuning for special cases.

the conventional CRF layer maximizes the probability of the only valid label sequence.


The conventional CRF layer maximizes the probability of the only valid label sequence.


Distant-LSTM-CRF (Giannakopoulos et al., 2017) has been proposed for the distantly supervised aspect term extraction, which can be viewed as an entity recognition task of a single type for business reviews.

we conduct experiments on the BC5CDR dataset by sampling different amounts of annotated articles for model training.


Empirically, we study the behavior of our system compared to published results.

in OTYPER (Yuan and Downey, 2018), each type embedding is computed by averaging the word embeddings of the words comprising the type.


Among the compatible fine types, we further filter the ones that have better support from the context.

our system, while not trained on any supervised data, achieves better or comparable performance compared to other supervised baselines in the out-of-domain evaluations.


Given an intermediate state s during SQG generation process, we may transit to multiple subsequent states s by applying different operations.

according the condition 1, we connect ( and (v 3 ,v 4 ).